code: def execute(cls, ctx, op):
      inp = ctx[op.inputs[0].key]
      result_length = inp.shape[0]
      unique_inputs = []
      for unique_input in op.inputs[1:]:
          unique_inputs.append(ctx[unique_input.key].tolist())
  
      if unique_inputs:
          if isinstance(inp, pd.Series):
              extra_series = pd.Series(unique_inputs[0])
              inp = pd.concat([inp, extra_series])
          else:
              # make all unique_input's length the same, then get a dataframe
              max_length = len(max(unique_inputs, key=len))
              unique_inputs = [
                  unique_list
                  + [unique_list[0]] * (max_length - len(unique_list))
                  for unique_list in unique_inputs
              ]
              extra_dataframe = pd.DataFrame(
                  dict(zip(op.columns, unique_inputs))
              )
  
              # add the columns that need not to encode, to concat extra_dataframe and inp
              total_columns = list(inp.columns.array)
              for col in op.columns:
                  total_columns.remove(col)
              remain_columns = total_columns
              not_encode_columns = []
              if len(remain_columns) > 0:
                  for col in remain_columns:
                      not_encode_columns.append(
                          [inp[col].iloc[0]] * max_length
                      )
              not_encode_dataframe = pd.DataFrame(
                  dict(zip(remain_columns, not_encode_columns))
              )
  
              extra_dataframe = pd.concat(
                  [not_encode_dataframe, extra_dataframe], axis=1
              )
              inp = pd.concat([inp, extra_dataframe], axis=0)
  
      result = pd.get_dummies(
          inp,
          op.prefix,
          op.prefix_sep,
          op.dummy_na,
          op.columns,
          op.sparse,
          op.drop_first,
          op.dtype,
      )
      ctx[op.outputs[0].key] = result.iloc[:result_length]
  
file_path: mars/mars/dataframe/base/get_dummies.py
imports: import numpy
  import pandas
  from ...core import OutputType, recursive_tile
  from ...serialization.serializables import StringField, BoolField, AnyField, ListField
  from ..core import SERIES_TYPE
  from ..datasource.dataframe import from_pandas
  from ..datasource.series import from_pandas
  from ..initializer import Series
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..reduction.unique import unique
  from ..utils import gen_unknown_index_value
label_desc: Use image node_id 3 for calling the DataFrameGetDummies obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types